high performance computing on graphics processing units: hgpu.org

hgpu.org » Tesla M40

Explainable Deep Behavioral Sequence Clustering for Transaction Fraud Detection

Wei Min, Weiming Liang, Hang Yin, Zhurong Wang, Mei Li, Alok Lal

View

Download (PDF)

Tags: Algorithms, Clustering, Deep learning, Finance, nVidia, Risk Management, Tesla M40

January 17, 2021 by hgpu

ISM2: Optimizing Irregular-Shaped Matrix-Matrix Multiplication on GPUs

Cody Rivera, Jieyang Chen, Nan Xiong, Shuaiwen Leon Song, Dingwen Tao

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, GEMM, Linear Algebra, Matrix multiplication, nVidia, Tesla K40, Tesla M40, Tesla P100

February 16, 2020 by hgpu

Block based Singular Value Decomposition approach to matrix factorization for recommender systems

Prasad Bhavana, Vikas Kumar, Vineet Padmanabhan

View

Download (PDF)

Source codes

Tags: AI, Algorithms, Computer science, CUDA, Factorization, Machine learning, nVidia, Package, Tesla M40

July 21, 2019 by hgpu

AccUDNN: A GPU Memory Efficient Accelerator for Training Ultra-deep Deep Neural Networks

Jinrong Guo, Wantao Liu, Wang Wang, Qu Lu, Songlin Hu, Jizhong Han, Ruixuan Li

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, Tesla M40

January 27, 2019 by hgpu

gSMat: A Scalable Sparse Matrix-based Join for SPARQL Query Processing

Xiaowang Zhang, Mingyue Zhang, Peng Peng, Jiaming Song, Zhiyong Feng, Lei Zou

View

Download (PDF)

Tags: Computer science, CUDA, Databases, nVidia, Sparse matrix, Tesla M40

July 28, 2018 by hgpu

Memory-Efficient Implementation of DenseNets

Geoff Pleiss, Danlu Chen, Gao Huang, Tongcheng Li, Laurens van der Maaten, Kilian Q. Weinberger

View

Download (PDF)

Source codes

Tags: Computer science, Computer vision, CUDA, Deep learning, nVidia, nVidia GeForce GTX Titan X, Package, Tesla M40, Torch

July 25, 2017 by hgpu

A Similarity Measure for GPU Kernel Subgraph Matching

Robert V. Lim, Boyana Norris, Allen D. Malony

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, Performance, Tesla K80, Tesla M40, Tesla P100

July 14, 2017 by hgpu

Fast MPEG-CDVS Encoder with GPU-CPU Hybrid Computing

Lingyu Duan, Wei Sun, Xinfeng Zhang, Jie Chen, Jianxiong Yin, Simon See, Tiejun Huang, Alex C. Kot, Wen Gao

View

Download (PDF)

Tags: Benchmarking, CUDA, Hybrid computing, Image processing, Neural networks, nVidia, nVidia GeForce GTX 1060, nVidia GeForce GTX 1080, nVidia GeForce GTX 1080 Ti, nVidia Tegra TX1, Tesla M40

June 1, 2017 by hgpu

cuTT: A High-Performance Tensor Transpose Library for CUDA Compatible GPUs

Antti-Pekka Hynninen, Dmitry I. Lyakh

View

Download (PDF)

Source codes

Tags: Algorithms, Benchmarking, Code generation, Computer science, CUDA, nVidia, Package, Tesla K20, Tesla K40, Tesla M40, Tesla P100

May 6, 2017 by hgpu

Autotuning GPU Kernels via Static and Predictive Analysis

Robert V. Lim, Boyana Norris, Allen D. Malony

View

Download (PDF)

Tags: Code generation, Computer science, CUDA, nVidia, Performance, PTX, Tesla K20, Tesla M2050, Tesla M40

February 2, 2017 by hgpu

Language Modeling with Gated Convolutional Networks

Yann N. Dauphin, Angela Fan, Michael Auli, David Grangier

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, NLP, nVidia, Performance, Tesla M40, Torch

December 26, 2016 by hgpu

Optimizing Performance of Recurrent Neural Networks on GPUs

Jeremy Appleyard, Tomas Kocisky, Phil Blunsom

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Deep learning, Lua, Neural networks, nVidia, Package, RNN, Tesla M40

April 9, 2016 by hgpu

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

Optical flow algorithms for SYCL

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

OpenMP5-Offload-OpenMC-Intel-PVC

Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Explainable Deep Behavioral Sequence Clustering for Transaction Fraud Detection

ISM2: Optimizing Irregular-Shaped Matrix-Matrix Multiplication on GPUs

Block based Singular Value Decomposition approach to matrix factorization for recommender systems

AccUDNN: A GPU Memory Efficient Accelerator for Training Ultra-deep Deep Neural Networks

gSMat: A Scalable Sparse Matrix-based Join for SPARQL Query Processing

Memory-Efficient Implementation of DenseNets

A Similarity Measure for GPU Kernel Subgraph Matching

Fast MPEG-CDVS Encoder with GPU-CPU Hybrid Computing

cuTT: A High-Performance Tensor Transpose Library for CUDA Compatible GPUs

Autotuning GPU Kernels via Static and Predictive Analysis

Language Modeling with Gated Convolutional Networks

Optimizing Performance of Recurrent Neural Networks on GPUs

Recent source codes

QArray

Celerity: High-level C++ for Accelerator Clusters

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

Optical flow algorithms for SYCL

OpenMP5-Offload-OpenMC-Intel-PVC

Most viewed papers (last 30 days)